Panoptic Quality (PQ) computation for binary masks.
Project description
panoptica
Computing instance-wise segmentation quality metrics for 2D and 3D semantic- and instance segmentation maps.
Features
The package provides three core modules:
- Instance Approximator: instance approximation algorithms to extract instances from semantic segmentation maps/model outputs.
- Instance Matcher: matches predicted instances with reference instances.
- Instance Evaluator: computes segmentation and detection quality metrics for pairs of predicted - and reference segmentation maps.
Installation
With a Python 3.10+ environment, you can install panoptica from pypi.org:
pip install panoptica
Use cases and tutorials
For tutorials featuring various use cases, cf. BrainLesion/tutorials/panoptica.
Semantic Segmentation Input
Although an instance-wise evaluation is highly relevant and desirable for many biomedical segmentation problems, they are still addressed as semantic segmentation problems due to the lack of appropriate instance labels.
This tutorial leverages all three modules.
Unmatched Instances Input
It is a common issue that instance segmentation outputs feature good outlines but mismatched instance labels. For this case, modules 2 and 3 can be utilized to match the instances and report metrics.
Matched Instances Input
If your predicted instances already match the reference instances, you can directly compute metrics with the third module, see Jupyter Notebook Example](https://github.com/BrainLesion/tutorials/tree/main/panoptica/example_spine_matched_instance.ipynb)
Citation
If you use panoptica in your research, please cite it to support the development!
TBA
upcoming citation
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